Review of Clinical Applications of Dual-Energy CT in Patients after Endovascular Aortic Repair

Kazimierczak, Wojciech; Kazimierczak, Natalia; Serafin, Zbigniew

doi:10.3390/jcm12247766

Cited by 8 publications

(9 citation statements)

References 96 publications

(130 reference statements)

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“…Projection-based MAR algorithms and dual-energy CT (DECT) are widely used due to their effectiveness in reducing photon starvation and beam-hardening artifacts. The use of high-photonenergy virtual monoenergetic images (VMIs) has proven effective in various applications [31][32][33]. There are initial, positive reports showing the application of these techniques in CBCT imaging [29,34].…”

Section: Discussionmentioning

confidence: 99%

The Impact of AI on Metal Artifacts in CBCT Oral Cavity Imaging

Wajer,

Kazimierczak

et al. 2024

Preprint

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Objective: This study aimed to assess the impact of artificial intelligence (AI)-driven noise reduction algorithms on metal artifacts and image quality parameters in cone-beam computed tomography (CBCT) images of the oral cavity. Materials and Methods: This retrospective study included 70 patients, 61 of whom were analyzed after excluding those with severe motion artifacts. CBCT scans, performed using a Hyperion X9 PRO 13 × 10 CBCT machine, included images with dental implants, amalgam fillings, orthodontic appliances, root canal fillings, and crowns. Images were processed with ClariCT. AI deep learning model (DLM) for noise reduction. Objective image quality was assessed using metrics such as the differentiation between voxel values (ΔVVs), the artifact index (AIx), and the contrast-to-noise ratio (CNR). Subjective assessments were performed by two experienced readers, who rated overall image quality and artifact intensity on predefined scales. Results: Compared with native images, DLM reconstructions significantly reduced the AIx and increased the CNR (p<0.001), indicating improved image clarity and artifact reduction. Subjective assessments also favored DLM images, with higher ratings for overall image quality and lower artifact intensity (p<0.001). However, the ΔVV values were similar between the native and DLM images, indicating that while the DLM reduced noise, it maintained the overall density distribution. Orthodontic appliances produced the most pronounced artifacts, while implants generated the least. Conclusion: AI-based noise reduction using ClariCT. AI significantly enhances CBCT image quality by reducing noise and metal artifacts, thereby improving diagnostic accuracy and treatment planning. Further research with larger, multicenter cohorts is recommended to validate these findings.

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Section: Discussionmentioning

confidence: 99%

The Impact of AI on Metal Artifacts in CBCT Oral Cavity Imaging

Wajer,

Kazimierczak

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Projection-based MAR algorithms and dual-energy CT (DECT) are widely used due to their effectiveness in reducing photon starvation and beam-hardening artifacts. The use of high-photon-energy virtual monoenergetic images (VMIs) has proven effective in various applications [ 30 , 31 , 32 ]. There are initial, positive reports showing the application of these techniques in CBCT imaging [ 28 , 33 ].…”

Section: Discussionmentioning

confidence: 99%

The Impact of AI on Metal Artifacts in CBCT Oral Cavity Imaging

Wajer,

Kazimierczak

et al. 2024

Diagnostics

Self Cite

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Objective: This study aimed to assess the impact of artificial intelligence (AI)-driven noise reduction algorithms on metal artifacts and image quality parameters in cone-beam computed tomography (CBCT) images of the oral cavity. Materials and Methods: This retrospective study included 70 patients, 61 of whom were analyzed after excluding those with severe motion artifacts. CBCT scans, performed using a Hyperion X9 PRO 13 × 10 CBCT machine, included images with dental implants, amalgam fillings, orthodontic appliances, root canal fillings, and crowns. Images were processed with the ClariCT.AI deep learning model (DLM) for noise reduction. Objective image quality was assessed using metrics such as the differentiation between voxel values (ΔVVs), the artifact index (AIx), and the contrast-to-noise ratio (CNR). Subjective assessments were performed by two experienced readers, who rated overall image quality and artifact intensity on predefined scales. Results: Compared with native images, DLM reconstructions significantly reduced the AIx and increased the CNR (p < 0.001), indicating improved image clarity and artifact reduction. Subjective assessments also favored DLM images, with higher ratings for overall image quality and lower artifact intensity (p < 0.001). However, the ΔVV values were similar between the native and DLM images, indicating that while the DLM reduced noise, it maintained the overall density distribution. Orthodontic appliances produced the most pronounced artifacts, while implants generated the least. Conclusions: AI-based noise reduction using ClariCT.AI significantly enhances CBCT image quality by reducing noise and metal artifacts, thereby improving diagnostic accuracy and treatment planning. Further research with larger, multicenter cohorts is recommended to validate these findings.

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“…Patients must be followed up with at 1, 6, and 12 months post-EVAR, and annually afterwards, to detect post-EVAR complications [ 6 , 21 , 67 ]. Currently, imaging modalities such as CTA, MRA or DUX are used in clinical practice to screen the patients after EVAR procedure, which are cost representative for the health-care system [ 18 , 19 , 68 , 69 ]. These techniques have different advantages and disadvantages (see Table 3 ) for EVAR surveillance [ 63 , 70 ].…”

Section: Evar Surveillancementioning

confidence: 99%

Implantable Pressure-Sensing Devices for Monitoring Abdominal Aortic Aneurysms in Post-Endovascular Aneurysm Repair

Silva,

Amin,

Dunne

et al. 2024

Sensors

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Over the past two decades, there has been extensive research into surveillance methods for the post-endovascular repair of abdominal aortic aneurysms, highlighting the importance of these technologies in supplementing or even replacing conventional image-screening modalities. This review aims to provide an overview of the current status of alternative surveillance solutions for endovascular aneurysm repair, while also identifying potential aneurysm features that could be used to develop novel monitoring technologies. It offers a comprehensive review of these recent clinical advances, comparing new and standard clinical practices. After introducing the clinical understanding of abdominal aortic aneurysms and exploring current treatment procedures, the paper discusses the current surveillance methods for endovascular repair, contrasting them with recent pressure-sensing technologies. The literature on three commercial pressure-sensing devices for post-endovascular repair surveillance is analyzed. Various pre-clinical and clinical studies assessing the safety and efficacy of these devices are reviewed, providing a comparative summary of their outcomes. The review of the results from pre-clinical and clinical studies suggests a consistent trend of decreased blood pressure in the excluded aneurysm sac post-repair. However, despite successful pressure readings from the aneurysm sac, no strong link has been established to translate these measurements into the presence or absence of endoleaks. Furthermore, the results do not allow for a conclusive determination of ongoing aneurysm sac growth. Consequently, a strong clinical need persists for monitoring endoleaks and aneurysm growth following endovascular repair.

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Review of Clinical Applications of Dual-Energy CT in Patients after Endovascular Aortic Repair

Cited by 8 publications

References 96 publications

The Impact of AI on Metal Artifacts in CBCT Oral Cavity Imaging

The Impact of AI on Metal Artifacts in CBCT Oral Cavity Imaging

The Impact of AI on Metal Artifacts in CBCT Oral Cavity Imaging

Implantable Pressure-Sensing Devices for Monitoring Abdominal Aortic Aneurysms in Post-Endovascular Aneurysm Repair

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